Set Of Efficient Rules Research Articles

Regulatory information transformation ruleset expansion to support automated building code compliance checking

The traditional building code compliance checking process mainly relies on design reviewers to review design documents or models manually. The intensive manual effort needed makes this process time-consuming, costly, and error-prone. Automated compliance checking (ACC) could be a promising upgrade of the traditional manual code compliance checking. With a reduced workload on design reviewers, ACC is cheaper, faster, and immune to human errors. To support ACC, building code requirements need to be represented in a computer-processable format to enable automated reasoning, which will in turn allow an automated assessment of the building design's compliance status with building codes. A major limitation of many existing ACC systems/methods is their limited range of checkable building code requirements. To address that, the state of the art uses pattern matching-based rules to transform building code requirements into computable formats automatically, but the ruleset was developed and tested only on a few chapters of building code requirements. An efficient ruleset expansion method is needed to increase its range of checkable building code requirements at a low cost, to bring ACC systems closer to full deployment. In this paper, the authors proposed a new regulatory information transformation ruleset expansion method for expanding an existing ruleset. This method can expand the range of checkable code requirements of ACC systems without significant manual effort. The proposed ruleset expansion method takes an iterative approach to ensure the generality and validity of new pattern matching-based rules and the quality of information transformation results. The expanded ruleset was tested on generating logic clauses from Chapter 5 of the International Building Code 2015. Compared to the baseline ruleset, the expanded ruleset increased the predicate-level precision, recall, and F1-score of the logic clause generation by 10.44%, 25.72%, and 18.02%, to 95.17%, 96.60%, and 95.88%, respectively.

COMPARISON OF ALGORITHMS BASED ON ROUGH SET THEORY FOR A 3-CLASS CLASSIFICATION

There are various data mining techniques to handle with huge amount of data sets. Rough set based classification provides an opportunity in the efficiency of algorithms when dealing with larger datasets. The selection of eligible attributes by using an efficient rule set offers decision makers save time and cost. This paper presents the comparison of the performance of the rough set based algorithms: Johnson’ s, Genetic Algorithm and Dynamic reducts. The performance of algorithms is measured based on accuracy, AUC and standard error for a 3-class classification problem on training on test data sets. Based on the test data, the results showed that genetic algorithm overperformed the others.

SDN Programming for Heterogeneous Switches with Flow Table Pipelining

High-level programming is one of the critical building blocks of the effective use of software-defined networking (SDN). Existing solutions, however, either (1) cannot utilize the state-of-the-art switches with flow table pipelining, a key technique to prevent flow rule set explosion or (2) force programmers to manually organize and manage hardware flow table pipelines, which is time-consuming and error-prone. This paper presents a high-level SDN programming framework to address these issues. The framework can automatically (1) generate rule sets for heterogeneous switches with different flow table pipelining designs and (2) update installed rules when the network state changes. As a result, the framework can not only generate efficient rule sets for switches but also provide programmers a centralized, intuitive, and hence easy-to-use programming API. Experiments show that the framework can generate compact rule sets that are 29–116 times smaller than those generated by other open-source SDN controllers. Besides, the framework is 5 times faster to recover from network link failures in comparison to other controllers.

Optimizing join enumeration in transformation-based query optimizers

Query optimizers built on the Volcano/Cascades framework, which is based on transformation rules, are used in many commercial databases. Transformation rulesets proposed earlier for join order enumeration in such a framework either allow enumeration of joins with cross-products (which can significantly increase the cost of optimization), or generate a large number of duplicate derivations. In this paper we propose two new rulesets for generating cross-product free trees. One of the rulesets is a minor extension of a simple but inefficient ruleset, which we prove is complete (we also show that a naive extension of an efficient ruleset leads to incompleteness). We then propose an efficient new ruleset, which is based on techniques proposed recently for top-down join order enumeration, but unlike earlier work it is cleanly integrated into the Volcano/Cascades framework, and can be used in conjunction with other transformation rules. We show that our ruleset is complete (i.e., it generates the entire search space without cross products) while avoiding inefficiency due to duplicate derivations. We have implemented this ruleset in the PyroJ Optimizer (an implementation of the Volcano optimizer framework) and show that it significantly outperforms the alternatives, in some cases by up to two orders of magnitude, in terms of time taken.

A Zone-Based Media Independent Information Service for IEEE 802.21 Networks

Next generation networks integrate different wireless technologies, including Wi-Fi, Wi-Max, and 3GPP (UMTS, HSPA, and/or LTE), in which the mobile node (MN) has the opportunity to switch from one network to another, under an always best connected scheme. In such heterogeneous environment, discovering which types of network connectivity and services are available is a critical challenge. The IEEE 802.21 standard specifies a network information server entity providing network information within a geographical area by which the MN can discover a service or a network. In this paper, we propose a zone-based media independent information service using the IEEE 802.21 standard to accelerate the neighbor discovery procedure. In the proposed scheme, the access networks are associated and grouped in mobility zones, through an efficient set of rules, to minimize the amount of control messages flowing in the core network. Through a NS-2 based simulation, the results demonstrate that the proposed scheme reduces the neighbor discovery delay as well as the signaling overhead if compared with the standard MIIS deployment.

A Hybrid of Self Organized Feature Maps and Parallel Genetic Algorithms for Uncertain Knowledge

The need to handle uncertainty and vagueness in real world becomes a necessity for developing good and efficient systems. Fuzzy rules and their usage in fuzzy systems help too much in solving these problems away from the complications of probability mathematical calculations. Fuzzy rules deals will words and labels instead of values of the variables. These labels are called variable’s subsets and needed to be prepared carefully to make sure that the fuzzy rules depend on accurate propositions. This research tries to design an efficient set of rules that is used later for inference by a hybrid model of Self Organized Features Maps and Parallel Genetic Algorithms. Self Organized Features Maps capabilities to cluster inputs using self adoption techniques have been very useful in generating fuzzy membership functions for the subsets of the fuzzy variables. Then the Parallel Genetic Algorithms use these membership functions along with the training data set to find the most fit fuzzy rule set from a number of initial sub populations according to the fitness function. The illustrations of the proposed model and its sub modules along with the experimental results and comparisons with previous techniques in generating rules from data sets are declared.

Improving MAC layer association through social-based metrics in mobile networks

Mobile Social Network (MSN) is a social network where one or more elements share interests or commonalities by connecting with one another using mobile devices. However, contrary to traditional online social networks, efficient communication and information exchanged depends on mobile users' context. In order to increase the scalability of the information exchange, network nodes need to be associated and grouped through an efficient set of rules and criterions, to minimize the time and amount of data messages flowing in the network. This article shows how social-based metrics can increase the efficiency of layer 2 associations in mobile networks, where nodes are able to join the network through their context information and probable interactions, optimizing the information exchange at MAC layer. Through a case study of mobile communications with different mobility patterns and dissemination approaches, we show that the social-based metrics increase overall network efficiency: maximize the percentage of information delivery, decrease the time to disseminate data traffic and reduce network usage.

The International Symposium on Ballistics

The International Symposium on Ballistics

Victims' rights and victims' wrongs: comparative liability in criminal law

Don't blame the is a cornerstone maxim of Anglo-American jurisprudence, but should the law generally ignore a victim's behavior in determining a defendant's liability? Victims' Rights and Victims' Wrongs criticizes the current criminal law approach and outlines a more fair, coherent, and efficient set of rules to recognize that victims sometimes co-author their own losses or injuries. Evaluating a number of controversial cases involving euthanasia, sadomasochism, date rape, battered wives, and innocent aggressors, Vera Bergelson builds a theoretical foundation for reform. Her approach to comparative criminal liability takes into account the actions of both the perpetrator and the victim and offers a unitary explanation for consent, self-defense, and provocation. This innovative book supplies a practical and coherent mechanism for evaluating the impact of a victim's conduct on a perpetrator's liability in a variety of circumstances, including those that are now artificially excluded from comparative analysis.

Parallel HEV Hybrid Controller Modeling for Power Management

In this paper, a parallel HEV hybrid controller is developed in the MATLAB/Simulink environment. Using the driver commands, the battery state of charge and the engine map, a set of efficient rules has been developed to efficiently split the power between the engine and the motor. The steps are: 1) Estimate the instantaneous torque demand. 2) Using the estimated torque, the feedback signals and the engine map, find the best operating point and then split the power and let the engine work as near as possible to this efficient point, that can be done by controlling the motor (or generator). In the case of motor, let its torque supply the rest of the torque needed, while the engine works near its efficient point, or in the case of generator, let its torque supply an additional load to put the engine in an efficient point. 3) Control the motor to supply the transient torque demand, and keep the engine torque constant as long time as possible, this help to reduce fuel consumption. Finally simulations of a conventional and hybrid vehicle are performed using Simulink environment to check the controller and series of results will prove the effectiveness of the proposed controller and will show the advantage of hybrid powertrain over conventional one in term of fuel economy.

Accuracy-based learning classification system

In order to implement a multi-category classification system, an efficient rule set is imperative for its investigation. In this paper, such a system is being introduced. In the first phase of its kind, the C4.5 rule induction algorithm is adopted to obtain useful rule set from classification problem, following a new data set partitioning approach. Next, the presented genetic algorithm (GA) is implemented to refine the learned rules in more efficient way. The resultant system has been compared with UCS (GA-based classification system) and C4.5 (non GA-based rule induction algorithm) on a number of benchmark data sets collected from UCI (University of California at Irvine) machine learning repository. Results demonstrate that the proposed genetic approach provides marked improvement in a number of cases.

Decomposition of Accident Loss and Efficiency of Liability Rules

AbstractThe main purpose of this paper is to show that the set of efficient rules which apportion liability between the victim and the tortfeasor is much larger than is generally believed to be the case. A larger set of efficient rules in general would have the implication of a less sharp conflict between economic efficiency on the one hand and non-efficiency normative criteria on the other. The condition of negligence liability which characterizes efficiency in the context of liability rules has an all-or-none character. Negligence liability requires that if one party is negligent and the other is not then the liability for the entire accident loss must fall on the negligent party. Thus within the framework of standard liability rules efficiency requirements preclude any non-efficiency considerations in situations where one party is negligent and the other is not. In this paper it is shown that accident loss can be decomposed into two parts such that while the apportionment of one part between the parties has bearing on efficiency, the apportionment of the other part has no efficiency implications. The part of accident loss which plays no role in providing appropriate incentives to the parties for taking due care can therefore be apportioned on non-efficiency considerations without in any way compromising the social goal of efficiency. For a systematic analysis of the requirements for efficiency, in this paper a notion more general than that of a liability rule, namely that of a decomposed liability rule, is introduced. A complete characterization of efficient decomposed liability rules is provided in the paper. The most important implication of the theorems of this paper is that by decomposing accident loss into two parts, the scope for non-efficiency considerations can be significantly broadened without sacrificing economic efficiency.

Decomposition of Accident Loss and Efficient Liability Rules

The main purpose of this paper is to show that the set of efficient rules which apportion liability between the victim and the tortfeasor is much larger than is generally believed to be the case. A larger set of efficient rules in general would have the implication of a less sharp conflict between economic efficiency on the one hand and non-efficiency normative criteria, including fairness and restitutive justice, on the other. The condition of negligence liability which characterizes efficiency in the context of liability rules has an all-or-none character. Negligence liability requires that if one party is negligent and the other is not then the liability for the entire accident loss must fall on the negligent party. Thus within the framework of standard liability rules efficiency requirements preclude any non-efficiency considerations in situations where one party is negligent and the other is not. In this paper it is shown that a part of accident loss plays no part in providing appropriate incentives to the parties for taking due care and can therefore be apportioned on non-efficiency considerations without in any way compromising the social goal of efficiency. For a systematic analysis of the requirements for efficiency, in this paper a notion more general than that of a liability rule, namely that of a decomposed liability rule, is introduced. A complete characterization of efficient decomposed liability rules is provided in the paper. The most important implication of the theorems of this paper is that by decomposing accident loss in two parts, the scope for non-efficiency considerations can be significantly broadened without sacrificing economic efficiency.

Retailer promotion planning: Improving forecast accuracy and interpretability

This article considers the supermarket manager's problem of forecasting demand for a product as a function of the product's attributes and of market control variables. To forecast sales on the stock keeping unit (SKU) level, a good model should account for product attributes, historical sales levels, and store specifics, and to control for marketing mix. One of the challenges here is that many variables which describe product, store, or promotion conditions are categorical with hundreds or thousands of levels in a single attribute. Identifying the right product attributes and incorporating them correctly into a prediction model is a very difficult statistical problem. This article proposes an analytical engine that combines techniques from statistical market response modeling, datamining, and combinatorial optimization to produce a small, efficient rule set that predicts sales volume levels.

Efficient Rules for Monetary Policy

This paper defines an efficient rule for monetary policy as one that minimizes a weighted sum of output variance and inflation variance. It derives several results about the efficiency of alternative rules in a simple macroeconomic model. First, efficient rules can be expressed as ‘Taylor rules’ in which interest rates respond to output and inflation. But the coefficients in efficient Taylor rules differ from the coefficients that fit actual policy in the United States. Second, inflation targets are efficient. Indeed, the set of efficient rules is equivalent to the set of inflation‐target policies with different speeds of adjustment. Finally, nominal‐income targets are highly inefficient: they create great volatility in both inflation and output.

Implementation and analysis of alternative algorithms for generalized shortest path problems

This paper addresses a special class of deterministic dynamic programming problems which can be formulated as a generalized network problem. Because of the similarities between this class of dynamic programming problems and shortest path problems, we are naming it the Generalized Shortest Path problem. A new primal extreme point algorithm and a new special dual algorithm are proposed here. While researchers have presented a variety of algorithms to solve this class of problems, there has been no comuptational analysis of these algorithms. An in-depth computational analysis is performed to determine the most efficient set of rules for implementing the algorithms of this paper.

The Delta System for synthesis rules

The Delta System is a computer system for developing synthesis rules. Building on nine years of experience with Hertz's SRS synthesis system [J. Acoust. Soc. Am. 72, 1155 (1982)], it gives users more flexible rule‐development facilities and enables them to produce considerably more practical rules. The Delta System provides a high‐level structured pattern matching language called Delta. The system achieves compact, efficient, and portable rule sets by compiling rules from Delta into a pseudo‐machine language. With Delta, rule‐writers can easily define and manipulate a broad class of utterance representations, ranging from linear strings to multi‐level structures. An utterance consists of multiple synchronized steams of user‐defined symbols and associated attributes. The symbols can represent phrases, words, morphs, demisyllables, phonemes, subphonemic units, etc. Delta's control structures and sophisticated pattern matching ease the writing of efficient rule sets. Symbols and attributes from all levels can be freely mixed in the patterns to be matched. When completed, the Delta System will accomodate almost any synthesis scheme, will support rule development for any language, will be extremely portable, and will produce compact, efficient rule sets.